sigmarsgarden
convolution-vision-transformers
| sigmarsgarden | convolution-vision-transformers | |
|---|---|---|
| 1 | 2 | |
| 3 | 210 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | almost 3 years ago | |
| Java | Python | |
| GNU Affero General Public License v3.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
sigmarsgarden
-
Sigmar's Garden Autosolver
The repository can be found here on GitHub.
convolution-vision-transformers
- has anyone here implemented Convolutional Vision Transformer (CvT)?
-
CvT: Introducing Convolutions to Vision Transformers
code: https://github.com/rishikksh20/convolution-vision-transformers
What are some alternatives?
PaddleViT - :robot: PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+
vit-pytorch - Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
Awesome-ComputerVision - Awesome-ComputerVision
MLP-Mixer-pytorch - Unofficial implementation of MLP-Mixer: An all-MLP Architecture for Vision
photonvision - PhotonVision is the free, fast, and easy-to-use computer vision solution for the FIRST Robotics Competition.
EasyCV - An all-in-one toolkit for computer vision
JavaCV - Java interface to OpenCV, FFmpeg, and more
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
sharpened-cosine-similarity - An alternative to convolution in neural networks
autogluon - Fast and Accurate ML in 3 Lines of Code
CvT - This is an official implementation of CvT: Introducing Convolutions to Vision Transformers.
classification - Classification of the MNIST dataset using various Deep Learning techniques